Distributed Shared Memory 편집하기

'''Distributed Shared Memory (DSM)''' is a memory management architecture that enables multiple distributed systems to share a logical memory space, allowing processes on different machines to access shared data as if they were on a single system.
==Overview==
Distributed shared memory provides an abstraction that simplifies parallel and distributed computing by:
*'''Providing a unified memory model:''' Applications access memory as if it were shared, even though it is physically distributed.
*'''Reducing explicit message passing:''' Eliminates the need for developers to manually implement interprocess communication (IPC).
*'''Ensuring consistency:''' Various memory consistency models define how updates to shared data are propagated.
==Key Features==
*'''Transparency:''' Abstracts away the complexities of distributed memory management.
*'''Scalability:''' Supports large-scale distributed applications.
*'''Consistency Models:''' Defines how and when changes in memory are visible to other nodes.
*'''Fault Tolerance:''' Can recover from failures by replicating memory across nodes.
==Memory Consistency Models==
Different consistency models define how updates to shared memory are observed:
{| class="wikitable"
!Consistency Model!!Description!!Example Use Case
|-
|'''Strict Consistency'''||Updates are immediately visible to all nodes.||Ideal but impractical in real-world distributed systems.
|-
|'''Sequential Consistency'''||All operations appear in some sequential order across all nodes.||Shared-memory multiprocessors.
|-
|'''Causal Consistency'''||Ensures that causally related memory updates appear in order.||Event-driven systems.
|-
|'''Release Consistency'''||Updates are visible after a synchronization operation (e.g., lock release).||High-performance computing.
|}
==Implementation Approaches==
Distributed shared memory can be implemented using different techniques:
*'''Hardware-Based DSM:''' Implements shared memory at the hardware level (e.g., NUMA systems).
*'''Software-Based DSM:''' Uses middleware or runtime systems to manage shared memory.
*'''Hybrid DSM:''' Combines hardware and software techniques for efficiency.
==Example DSM Systems==
*'''TreadMarks:''' A software DSM system supporting relaxed memory consistency.
*'''Memcached:''' A distributed caching system that can function as a shared memory abstraction.
*'''Spark RDDs:''' In-memory data sharing in distributed computing environments.
*'''IBM Coherence:''' A distributed in-memory data grid.
==Comparison with Other Memory Models==
{| class="wikitable"
!Feature!!Distributed Shared Memory!!Message Passing!!Centralized Shared Memory
|-
|'''Abstraction Level'''||High||Low||High
|-
|'''Communication Method'''||Implicit memory access||Explicit message exchange||Direct memory access
|-
|'''Performance'''||Moderate||High||High
|-
|'''Fault Tolerance'''||High (via replication)||High (via redundancy)||Low
|}
==Advantages==
*Simplifies parallel programming by abstracting memory distribution.
*Reduces the need for explicit communication mechanisms.
*Provides a scalable solution for distributed computing applications.
==Limitations==
*Memory consistency overhead can degrade performance.
*Higher latency compared to local shared memory due to network communication.
*Complexity in ensuring fault tolerance and replication.
==Applications==
*'''High-Performance Computing (HPC):''' Used in large-scale parallel computing systems.
*'''Distributed Databases:''' Enables shared access to distributed in-memory storage.
*'''Cloud Computing:''' Used in distributed caching and shared resource management.
*'''Big Data Processing:''' Facilitates in-memory data sharing in frameworks like Apache Spark.
==See Also==
*[[Shared Memory]]
*[[Message Passing Interface (MPI)]]
*[[Parallel Computing]]
*[[Cloud Computing]]
*[[Big Data Processing]]
*[[NUMA (Non-Uniform Memory Access)]]
[[분류:Distributed Computing]]